org.apache.flink.streaming.api.datastream.AllWindowedStream Maven / Gradle / Ivy
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.flink.streaming.api.datastream;
import org.apache.flink.annotation.Public;
import org.apache.flink.annotation.PublicEvolving;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.common.functions.RichFunction;
import org.apache.flink.api.common.state.AggregatingStateDescriptor;
import org.apache.flink.api.common.state.ListStateDescriptor;
import org.apache.flink.api.common.state.ReducingStateDescriptor;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.common.typeutils.TypeSerializer;
import org.apache.flink.api.java.Utils;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.functions.NullByteKeySelector;
import org.apache.flink.api.java.typeutils.TypeExtractor;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.aggregation.AggregationFunction;
import org.apache.flink.streaming.api.functions.aggregation.ComparableAggregator;
import org.apache.flink.streaming.api.functions.aggregation.SumAggregator;
import org.apache.flink.streaming.api.functions.windowing.AggregateApplyAllWindowFunction;
import org.apache.flink.streaming.api.functions.windowing.AllWindowFunction;
import org.apache.flink.streaming.api.functions.windowing.PassThroughAllWindowFunction;
import org.apache.flink.streaming.api.functions.windowing.ProcessAllWindowFunction;
import org.apache.flink.streaming.api.functions.windowing.ReduceApplyAllWindowFunction;
import org.apache.flink.streaming.api.functions.windowing.ReduceApplyProcessAllWindowFunction;
import org.apache.flink.streaming.api.operators.OneInputStreamOperator;
import org.apache.flink.streaming.api.windowing.assigners.MergingWindowAssigner;
import org.apache.flink.streaming.api.windowing.assigners.WindowAssigner;
import org.apache.flink.streaming.api.windowing.evictors.Evictor;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.triggers.Trigger;
import org.apache.flink.streaming.api.windowing.windows.Window;
import org.apache.flink.streaming.runtime.operators.windowing.EvictingWindowOperator;
import org.apache.flink.streaming.runtime.operators.windowing.WindowOperator;
import org.apache.flink.streaming.runtime.operators.windowing.functions.InternalAggregateProcessAllWindowFunction;
import org.apache.flink.streaming.runtime.operators.windowing.functions.InternalIterableAllWindowFunction;
import org.apache.flink.streaming.runtime.operators.windowing.functions.InternalIterableProcessAllWindowFunction;
import org.apache.flink.streaming.runtime.operators.windowing.functions.InternalSingleValueAllWindowFunction;
import org.apache.flink.streaming.runtime.operators.windowing.functions.InternalSingleValueProcessAllWindowFunction;
import org.apache.flink.streaming.runtime.operators.windowing.functions.InternalWindowFunction;
import org.apache.flink.streaming.runtime.streamrecord.StreamElementSerializer;
import org.apache.flink.streaming.runtime.streamrecord.StreamRecord;
import org.apache.flink.util.OutputTag;
import org.apache.flink.util.Preconditions;
import java.time.Duration;
import static org.apache.flink.util.Preconditions.checkArgument;
import static org.apache.flink.util.Preconditions.checkNotNull;
/**
* A {@code AllWindowedStream} represents a data stream where the stream of elements is split into
* windows based on a {@link org.apache.flink.streaming.api.windowing.assigners.WindowAssigner}.
* Window emission is triggered based on a {@link
* org.apache.flink.streaming.api.windowing.triggers.Trigger}.
*
* If an {@link org.apache.flink.streaming.api.windowing.evictors.Evictor} is specified it will
* be used to evict elements from the window after evaluation was triggered by the {@code Trigger}
* but before the actual evaluation of the window. When using an evictor, window performance will
* degrade significantly, since pre-aggregation of window results cannot be used.
*
*
Note that the {@code AllWindowedStream} is purely an API construct, during runtime the {@code
* AllWindowedStream} will be collapsed together with the operation over the window into one single
* operation.
*
* @param The type of elements in the stream.
* @param The type of {@code Window} that the {@code WindowAssigner} assigns the elements to.
*/
@Public
public class AllWindowedStream {
/** The keyed data stream that is windowed by this stream. */
private final KeyedStream input;
/** The window assigner. */
private final WindowAssigner windowAssigner;
/** The trigger that is used for window evaluation/emission. */
private Trigger trigger;
/** The evictor that is used for evicting elements before window evaluation. */
private Evictor evictor;
/** The user-specified allowed lateness. */
private long allowedLateness = 0L;
/**
* Side output {@code OutputTag} for late data. If no tag is set late data will simply be
* dropped.
*/
private OutputTag lateDataOutputTag;
@PublicEvolving
public AllWindowedStream(DataStream input, WindowAssigner windowAssigner) {
this.input = input.keyBy(new NullByteKeySelector());
this.windowAssigner = windowAssigner;
this.trigger = windowAssigner.getDefaultTrigger();
}
/** Sets the {@code Trigger} that should be used to trigger window emission. */
@PublicEvolving
public AllWindowedStream trigger(Trigger trigger) {
if (windowAssigner instanceof MergingWindowAssigner && !trigger.canMerge()) {
throw new UnsupportedOperationException(
"A merging window assigner cannot be used with a trigger that does not support merging.");
}
this.trigger = trigger;
return this;
}
/**
* Sets the time by which elements are allowed to be late. Elements that arrive behind the
* watermark by more than the specified time will be dropped. By default, the allowed lateness
* is {@code 0L}.
*
* Setting an allowed lateness is only valid for event-time windows.
*
* @deprecated Use {@link #allowedLateness(Duration)}, instead.
*/
@Deprecated
@PublicEvolving
public AllWindowedStream allowedLateness(Time lateness) {
return allowedLateness(lateness.toDuration());
}
/**
* Sets the time by which elements are allowed to be late. Elements that arrive behind the
* watermark by more than the specified time will be dropped. By default, the allowed lateness
* is {@code 0L}.
*
* Setting an allowed lateness is only valid for event-time windows.
*/
@PublicEvolving
public AllWindowedStream allowedLateness(Duration lateness) {
final long millis = lateness.toMillis();
checkArgument(millis >= 0, "The allowed lateness cannot be negative.");
this.allowedLateness = millis;
return this;
}
/**
* Send late arriving data to the side output identified by the given {@link OutputTag}. Data is
* considered late after the watermark has passed the end of the window plus the allowed
* lateness set using {@link #allowedLateness(Duration)}.
*
* You can get the stream of late data using {@link
* SingleOutputStreamOperator#getSideOutput(OutputTag)} on the {@link
* SingleOutputStreamOperator} resulting from the windowed operation with the same {@link
* OutputTag}.
*/
@PublicEvolving
public AllWindowedStream sideOutputLateData(OutputTag outputTag) {
Preconditions.checkNotNull(outputTag, "Side output tag must not be null.");
this.lateDataOutputTag = input.getExecutionEnvironment().clean(outputTag);
return this;
}
/**
* Sets the {@code Evictor} that should be used to evict elements from a window before emission.
*
* Note: When using an evictor window performance will degrade significantly, since
* incremental aggregation of window results cannot be used.
*/
@PublicEvolving
public AllWindowedStream evictor(Evictor evictor) {
this.evictor = evictor;
return this;
}
// ------------------------------------------------------------------------
// Operations on the keyed windows
// ------------------------------------------------------------------------
/**
* Applies a reduce function to the window. The window function is called for each evaluation of
* the window for each key individually. The output of the reduce function is interpreted as a
* regular non-windowed stream.
*
* This window will try and incrementally aggregate data as much as the window policies
* permit. For example, tumbling time windows can aggregate the data, meaning that only one
* element per key is stored. Sliding time windows will aggregate on the granularity of the
* slide interval, so a few elements are stored per key (one per slide interval). Custom windows
* may not be able to incrementally aggregate, or may need to store extra values in an
* aggregation tree.
*
* @param function The reduce function.
* @return The data stream that is the result of applying the reduce function to the window.
*/
@SuppressWarnings("unchecked")
public SingleOutputStreamOperator reduce(ReduceFunction function) {
if (function instanceof RichFunction) {
throw new UnsupportedOperationException(
"ReduceFunction of reduce can not be a RichFunction. "
+ "Please use reduce(ReduceFunction, WindowFunction) instead.");
}
// clean the closure
function = input.getExecutionEnvironment().clean(function);
String callLocation = Utils.getCallLocationName();
String udfName = "AllWindowedStream." + callLocation;
return reduce(function, new PassThroughAllWindowFunction());
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window for each key individually. The output of the window function is
* interpreted as a regular non-windowed stream.
*
* Arriving data is incrementally aggregated using the given reducer.
*
* @param reduceFunction The reduce function that is used for incremental aggregation.
* @param function The window function.
* @return The data stream that is the result of applying the window function to the window.
*/
@PublicEvolving
public SingleOutputStreamOperator reduce(
ReduceFunction reduceFunction, AllWindowFunction function) {
TypeInformation inType = input.getType();
TypeInformation resultType = getAllWindowFunctionReturnType(function, inType);
return reduce(reduceFunction, function, resultType);
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window for each key individually. The output of the window function is
* interpreted as a regular non-windowed stream.
*
* Arriving data is incrementally aggregated using the given reducer.
*
* @param reduceFunction The reduce function that is used for incremental aggregation.
* @param function The window function.
* @param resultType Type information for the result type of the window function
* @return The data stream that is the result of applying the window function to the window.
*/
@PublicEvolving
public SingleOutputStreamOperator reduce(
ReduceFunction reduceFunction,
AllWindowFunction function,
TypeInformation resultType) {
if (reduceFunction instanceof RichFunction) {
throw new UnsupportedOperationException(
"ReduceFunction of reduce can not be a RichFunction.");
}
// clean the closures
function = input.getExecutionEnvironment().clean(function);
reduceFunction = input.getExecutionEnvironment().clean(reduceFunction);
String callLocation = Utils.getCallLocationName();
String udfName = "AllWindowedStream." + callLocation;
String opName = windowAssigner.getClass().getSimpleName();
String opDescription;
KeySelector keySel = input.getKeySelector();
OneInputStreamOperator operator;
if (evictor != null) {
@SuppressWarnings({"unchecked", "rawtypes"})
TypeSerializer> streamRecordSerializer =
(TypeSerializer>)
new StreamElementSerializer(
input.getType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()));
ListStateDescriptor> stateDesc =
new ListStateDescriptor<>("window-contents", streamRecordSerializer);
opDescription =
"TriggerWindow("
+ windowAssigner
+ ", "
+ stateDesc
+ ", "
+ trigger
+ ", "
+ evictor
+ ", "
+ udfName
+ ")";
operator =
new EvictingWindowOperator<>(
windowAssigner,
windowAssigner.getWindowSerializer(
getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()),
stateDesc,
new InternalIterableAllWindowFunction<>(
new ReduceApplyAllWindowFunction<>(reduceFunction, function)),
trigger,
evictor,
allowedLateness,
lateDataOutputTag);
} else {
ReducingStateDescriptor stateDesc =
new ReducingStateDescriptor<>(
"window-contents",
reduceFunction,
input.getType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()));
opDescription =
"TriggerWindow("
+ windowAssigner
+ ", "
+ stateDesc
+ ", "
+ trigger
+ ", "
+ udfName
+ ")";
operator =
new WindowOperator<>(
windowAssigner,
windowAssigner.getWindowSerializer(
getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()),
stateDesc,
new InternalSingleValueAllWindowFunction<>(function),
trigger,
allowedLateness,
lateDataOutputTag);
}
return input.transform(opName, resultType, operator)
.setDescription(opDescription)
.forceNonParallel();
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window for each key individually. The output of the window function is
* interpreted as a regular non-windowed stream.
*
* Arriving data is incrementally aggregated using the given reducer.
*
* @param reduceFunction The reduce function that is used for incremental aggregation.
* @param function The process window function.
* @return The data stream that is the result of applying the window function to the window.
*/
@PublicEvolving
public SingleOutputStreamOperator reduce(
ReduceFunction reduceFunction, ProcessAllWindowFunction function) {
TypeInformation resultType =
getProcessAllWindowFunctionReturnType(function, input.getType());
return reduce(reduceFunction, function, resultType);
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window for each key individually. The output of the window function is
* interpreted as a regular non-windowed stream.
*
* Arriving data is incrementally aggregated using the given reducer.
*
* @param reduceFunction The reduce function that is used for incremental aggregation.
* @param function The process window function.
* @param resultType Type information for the result type of the window function
* @return The data stream that is the result of applying the window function to the window.
*/
@PublicEvolving
public SingleOutputStreamOperator reduce(
ReduceFunction reduceFunction,
ProcessAllWindowFunction function,
TypeInformation resultType) {
if (reduceFunction instanceof RichFunction) {
throw new UnsupportedOperationException(
"ReduceFunction of reduce can not be a RichFunction.");
}
// clean the closures
function = input.getExecutionEnvironment().clean(function);
reduceFunction = input.getExecutionEnvironment().clean(reduceFunction);
String callLocation = Utils.getCallLocationName();
String udfName = "AllWindowedStream." + callLocation;
String opName;
KeySelector keySel = input.getKeySelector();
OneInputStreamOperator operator;
if (evictor != null) {
@SuppressWarnings({"unchecked", "rawtypes"})
TypeSerializer> streamRecordSerializer =
(TypeSerializer>)
new StreamElementSerializer(
input.getType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()));
ListStateDescriptor> stateDesc =
new ListStateDescriptor<>("window-contents", streamRecordSerializer);
opName =
"TriggerWindow("
+ windowAssigner
+ ", "
+ stateDesc
+ ", "
+ trigger
+ ", "
+ evictor
+ ", "
+ udfName
+ ")";
operator =
new EvictingWindowOperator<>(
windowAssigner,
windowAssigner.getWindowSerializer(
getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()),
stateDesc,
new InternalIterableProcessAllWindowFunction<>(
new ReduceApplyProcessAllWindowFunction<>(
reduceFunction, function)),
trigger,
evictor,
allowedLateness,
lateDataOutputTag);
} else {
ReducingStateDescriptor stateDesc =
new ReducingStateDescriptor<>(
"window-contents",
reduceFunction,
input.getType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()));
opName =
"TriggerWindow("
+ windowAssigner
+ ", "
+ stateDesc
+ ", "
+ trigger
+ ", "
+ udfName
+ ")";
operator =
new WindowOperator<>(
windowAssigner,
windowAssigner.getWindowSerializer(
getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()),
stateDesc,
new InternalSingleValueProcessAllWindowFunction<>(function),
trigger,
allowedLateness,
lateDataOutputTag);
}
return input.transform(opName, resultType, operator).forceNonParallel();
}
// ------------------------------------------------------------------------
// AggregateFunction
// ------------------------------------------------------------------------
/**
* Applies the given {@code AggregateFunction} to each window. The AggregateFunction aggregates
* all elements of a window into a single result element. The stream of these result elements
* (one per window) is interpreted as a regular non-windowed stream.
*
* @param function The aggregation function.
* @return The data stream that is the result of applying the fold function to the window.
* @param The type of the AggregateFunction's accumulator
* @param The type of the elements in the resulting stream, equal to the AggregateFunction's
* result type
*/
@PublicEvolving
public SingleOutputStreamOperator aggregate(AggregateFunction function) {
checkNotNull(function, "function");
if (function instanceof RichFunction) {
throw new UnsupportedOperationException(
"This aggregation function cannot be a RichFunction.");
}
TypeInformation accumulatorType =
TypeExtractor.getAggregateFunctionAccumulatorType(
function, input.getType(), null, false);
TypeInformation resultType =
TypeExtractor.getAggregateFunctionReturnType(
function, input.getType(), null, false);
return aggregate(function, accumulatorType, resultType);
}
/**
* Applies the given {@code AggregateFunction} to each window. The AggregateFunction aggregates
* all elements of a window into a single result element. The stream of these result elements
* (one per window) is interpreted as a regular non-windowed stream.
*
* @param function The aggregation function.
* @return The data stream that is the result of applying the aggregation function to the
* window.
* @param The type of the AggregateFunction's accumulator
* @param The type of the elements in the resulting stream, equal to the AggregateFunction's
* result type
*/
@PublicEvolving
public SingleOutputStreamOperator aggregate(
AggregateFunction function,
TypeInformation accumulatorType,
TypeInformation resultType) {
checkNotNull(function, "function");
checkNotNull(accumulatorType, "accumulatorType");
checkNotNull(resultType, "resultType");
if (function instanceof RichFunction) {
throw new UnsupportedOperationException(
"This aggregation function cannot be a RichFunction.");
}
return aggregate(
function, new PassThroughAllWindowFunction(), accumulatorType, resultType);
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window for each key individually. The output of the window function is
* interpreted as a regular non-windowed stream.
*
* Arriving data is incrementally aggregated using the given aggregate function. This means
* that the window function typically has only a single value to process when called.
*
* @param aggFunction The aggregate function that is used for incremental aggregation.
* @param windowFunction The window function.
* @return The data stream that is the result of applying the window function to the window.
* @param The type of the AggregateFunction's accumulator
* @param The type of AggregateFunction's result, and the WindowFunction's input
* @param The type of the elements in the resulting stream, equal to the WindowFunction's
* result type
*/
@PublicEvolving
public SingleOutputStreamOperator aggregate(
AggregateFunction aggFunction, AllWindowFunction windowFunction) {
checkNotNull(aggFunction, "aggFunction");
checkNotNull(windowFunction, "windowFunction");
TypeInformation accumulatorType =
TypeExtractor.getAggregateFunctionAccumulatorType(
aggFunction, input.getType(), null, false);
TypeInformation aggResultType =
TypeExtractor.getAggregateFunctionReturnType(
aggFunction, input.getType(), null, false);
TypeInformation resultType =
getAllWindowFunctionReturnType(windowFunction, aggResultType);
return aggregate(aggFunction, windowFunction, accumulatorType, resultType);
}
private static TypeInformation getAllWindowFunctionReturnType(
AllWindowFunction function, TypeInformation inType) {
return TypeExtractor.getUnaryOperatorReturnType(
function, AllWindowFunction.class, 0, 1, new int[] {2, 0}, inType, null, false);
}
private static TypeInformation getProcessAllWindowFunctionReturnType(
ProcessAllWindowFunction function, TypeInformation inType) {
return TypeExtractor.getUnaryOperatorReturnType(
function,
ProcessAllWindowFunction.class,
0,
1,
TypeExtractor.NO_INDEX,
inType,
null,
false);
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window for each key individually. The output of the window function is
* interpreted as a regular non-windowed stream.
*
* Arriving data is incrementally aggregated using the given aggregate function. This means
* that the window function typically has only a single value to process when called.
*
* @param aggregateFunction The aggregation function that is used for incremental aggregation.
* @param windowFunction The window function.
* @param accumulatorType Type information for the internal accumulator type of the aggregation
* function
* @param resultType Type information for the result type of the window function
* @return The data stream that is the result of applying the window function to the window.
* @param The type of the AggregateFunction's accumulator
* @param The type of AggregateFunction's result, and the WindowFunction's input
* @param The type of the elements in the resulting stream, equal to the WindowFunction's
* result type
*/
@PublicEvolving
public SingleOutputStreamOperator aggregate(
AggregateFunction aggregateFunction,
AllWindowFunction windowFunction,
TypeInformation accumulatorType,
TypeInformation resultType) {
checkNotNull(aggregateFunction, "aggregateFunction");
checkNotNull(windowFunction, "windowFunction");
checkNotNull(accumulatorType, "accumulatorType");
checkNotNull(resultType, "resultType");
if (aggregateFunction instanceof RichFunction) {
throw new UnsupportedOperationException(
"This aggregate function cannot be a RichFunction.");
}
// clean the closures
windowFunction = input.getExecutionEnvironment().clean(windowFunction);
aggregateFunction = input.getExecutionEnvironment().clean(aggregateFunction);
final String callLocation = Utils.getCallLocationName();
final String udfName = "AllWindowedStream." + callLocation;
final String opName;
final KeySelector keySel = input.getKeySelector();
OneInputStreamOperator operator;
if (evictor != null) {
@SuppressWarnings({"unchecked", "rawtypes"})
TypeSerializer> streamRecordSerializer =
(TypeSerializer>)
new StreamElementSerializer(
input.getType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()));
ListStateDescriptor> stateDesc =
new ListStateDescriptor<>("window-contents", streamRecordSerializer);
opName =
"TriggerWindow("
+ windowAssigner
+ ", "
+ stateDesc
+ ", "
+ trigger
+ ", "
+ evictor
+ ", "
+ udfName
+ ")";
operator =
new EvictingWindowOperator<>(
windowAssigner,
windowAssigner.getWindowSerializer(
getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()),
stateDesc,
new InternalIterableAllWindowFunction<>(
new AggregateApplyAllWindowFunction<>(
aggregateFunction, windowFunction)),
trigger,
evictor,
allowedLateness,
lateDataOutputTag);
} else {
AggregatingStateDescriptor stateDesc =
new AggregatingStateDescriptor<>(
"window-contents",
aggregateFunction,
accumulatorType.createSerializer(
getExecutionEnvironment().getConfig().getSerializerConfig()));
opName =
"TriggerWindow("
+ windowAssigner
+ ", "
+ stateDesc
+ ", "
+ trigger
+ ", "
+ udfName
+ ")";
operator =
new WindowOperator<>(
windowAssigner,
windowAssigner.getWindowSerializer(
getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()),
stateDesc,
new InternalSingleValueAllWindowFunction<>(windowFunction),
trigger,
allowedLateness,
lateDataOutputTag);
}
return input.transform(opName, resultType, operator).forceNonParallel();
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window for each key individually. The output of the window function is
* interpreted as a regular non-windowed stream.
*
* Arriving data is incrementally aggregated using the given aggregate function. This means
* that the window function typically has only a single value to process when called.
*
* @param aggFunction The aggregate function that is used for incremental aggregation.
* @param windowFunction The process window function.
* @return The data stream that is the result of applying the window function to the window.
* @param The type of the AggregateFunction's accumulator
* @param The type of AggregateFunction's result, and the WindowFunction's input
* @param The type of the elements in the resulting stream, equal to the WindowFunction's
* result type
*/
@PublicEvolving
public SingleOutputStreamOperator aggregate(
AggregateFunction aggFunction,
ProcessAllWindowFunction windowFunction) {
checkNotNull(aggFunction, "aggFunction");
checkNotNull(windowFunction, "windowFunction");
TypeInformation accumulatorType =
TypeExtractor.getAggregateFunctionAccumulatorType(
aggFunction, input.getType(), null, false);
TypeInformation aggResultType =
TypeExtractor.getAggregateFunctionReturnType(
aggFunction, input.getType(), null, false);
TypeInformation resultType =
getProcessAllWindowFunctionReturnType(windowFunction, aggResultType);
return aggregate(aggFunction, windowFunction, accumulatorType, aggResultType, resultType);
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window for each key individually. The output of the window function is
* interpreted as a regular non-windowed stream.
*
* Arriving data is incrementally aggregated using the given aggregate function. This means
* that the window function typically has only a single value to process when called.
*
* @param aggregateFunction The aggregation function that is used for incremental aggregation.
* @param windowFunction The process window function.
* @param accumulatorType Type information for the internal accumulator type of the aggregation
* function
* @param resultType Type information for the result type of the window function
* @return The data stream that is the result of applying the window function to the window.
* @param The type of the AggregateFunction's accumulator
* @param The type of AggregateFunction's result, and the WindowFunction's input
* @param The type of the elements in the resulting stream, equal to the WindowFunction's
* result type
*/
@PublicEvolving
public SingleOutputStreamOperator aggregate(
AggregateFunction aggregateFunction,
ProcessAllWindowFunction windowFunction,
TypeInformation accumulatorType,
TypeInformation aggregateResultType,
TypeInformation resultType) {
checkNotNull(aggregateFunction, "aggregateFunction");
checkNotNull(windowFunction, "windowFunction");
checkNotNull(accumulatorType, "accumulatorType");
checkNotNull(aggregateResultType, "aggregateResultType");
checkNotNull(resultType, "resultType");
if (aggregateFunction instanceof RichFunction) {
throw new UnsupportedOperationException(
"This aggregate function cannot be a RichFunction.");
}
// clean the closures
windowFunction = input.getExecutionEnvironment().clean(windowFunction);
aggregateFunction = input.getExecutionEnvironment().clean(aggregateFunction);
final String callLocation = Utils.getCallLocationName();
final String udfName = "AllWindowedStream." + callLocation;
final String opName = windowAssigner.getClass().getSimpleName();
final String opDescription;
final KeySelector keySel = input.getKeySelector();
OneInputStreamOperator operator;
if (evictor != null) {
@SuppressWarnings({"unchecked", "rawtypes"})
TypeSerializer> streamRecordSerializer =
(TypeSerializer>)
new StreamElementSerializer(
input.getType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()));
ListStateDescriptor> stateDesc =
new ListStateDescriptor<>("window-contents", streamRecordSerializer);
opDescription =
"TriggerWindow("
+ windowAssigner
+ ", "
+ stateDesc
+ ", "
+ trigger
+ ", "
+ evictor
+ ", "
+ udfName
+ ")";
operator =
new EvictingWindowOperator<>(
windowAssigner,
windowAssigner.getWindowSerializer(
getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()),
stateDesc,
new InternalAggregateProcessAllWindowFunction<>(
aggregateFunction, windowFunction),
trigger,
evictor,
allowedLateness,
lateDataOutputTag);
} else {
AggregatingStateDescriptor stateDesc =
new AggregatingStateDescriptor<>(
"window-contents",
aggregateFunction,
accumulatorType.createSerializer(
getExecutionEnvironment().getConfig().getSerializerConfig()));
opDescription =
"TriggerWindow("
+ windowAssigner
+ ", "
+ stateDesc
+ ", "
+ trigger
+ ", "
+ udfName
+ ")";
operator =
new WindowOperator<>(
windowAssigner,
windowAssigner.getWindowSerializer(
getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()),
stateDesc,
new InternalSingleValueProcessAllWindowFunction<>(windowFunction),
trigger,
allowedLateness,
lateDataOutputTag);
}
return input.transform(opName, resultType, operator)
.setDescription(opDescription)
.forceNonParallel();
}
// ------------------------------------------------------------------------
// Apply (Window Function)
// ------------------------------------------------------------------------
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window. The output of the window function is interpreted as a regular
* non-windowed stream.
*
* Note that this function requires that all data in the windows is buffered until the window
* is evaluated, as the function provides no means of incremental aggregation.
*
* @param function The window function.
* @return The data stream that is the result of applying the window function to the window.
*/
public SingleOutputStreamOperator apply(AllWindowFunction function) {
String callLocation = Utils.getCallLocationName();
function = input.getExecutionEnvironment().clean(function);
TypeInformation resultType = getAllWindowFunctionReturnType(function, getInputType());
return apply(new InternalIterableAllWindowFunction<>(function), resultType, callLocation);
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window. The output of the window function is interpreted as a regular
* non-windowed stream.
*
* Note that this function requires that all data in the windows is buffered until the window
* is evaluated, as the function provides no means of incremental aggregation.
*
* @param function The window function.
* @return The data stream that is the result of applying the window function to the window.
*/
public SingleOutputStreamOperator apply(
AllWindowFunction function, TypeInformation resultType) {
String callLocation = Utils.getCallLocationName();
function = input.getExecutionEnvironment().clean(function);
return apply(new InternalIterableAllWindowFunction<>(function), resultType, callLocation);
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window. The output of the window function is interpreted as a regular
* non-windowed stream.
*
* Note that this function requires that all data in the windows is buffered until the window
* is evaluated, as the function provides no means of incremental aggregation.
*
* @param function The process window function.
* @return The data stream that is the result of applying the window function to the window.
*/
@PublicEvolving
public SingleOutputStreamOperator process(ProcessAllWindowFunction function) {
String callLocation = Utils.getCallLocationName();
function = input.getExecutionEnvironment().clean(function);
TypeInformation resultType =
getProcessAllWindowFunctionReturnType(function, getInputType());
return apply(
new InternalIterableProcessAllWindowFunction<>(function), resultType, callLocation);
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window. The output of the window function is interpreted as a regular
* non-windowed stream.
*
* Note that this function requires that all data in the windows is buffered until the window
* is evaluated, as the function provides no means of incremental aggregation.
*
* @param function The process window function.
* @return The data stream that is the result of applying the window function to the window.
*/
@PublicEvolving
public SingleOutputStreamOperator process(
ProcessAllWindowFunction function, TypeInformation resultType) {
String callLocation = Utils.getCallLocationName();
function = input.getExecutionEnvironment().clean(function);
return apply(
new InternalIterableProcessAllWindowFunction<>(function), resultType, callLocation);
}
private SingleOutputStreamOperator apply(
InternalWindowFunction, R, Byte, W> function,
TypeInformation resultType,
String callLocation) {
String udfName = "AllWindowedStream." + callLocation;
String opName;
KeySelector keySel = input.getKeySelector();
WindowOperator, R, W> operator;
if (evictor != null) {
@SuppressWarnings({"unchecked", "rawtypes"})
TypeSerializer> streamRecordSerializer =
(TypeSerializer>)
new StreamElementSerializer(
input.getType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()));
ListStateDescriptor> stateDesc =
new ListStateDescriptor<>("window-contents", streamRecordSerializer);
opName =
"TriggerWindow("
+ windowAssigner
+ ", "
+ stateDesc
+ ", "
+ trigger
+ ", "
+ evictor
+ ", "
+ udfName
+ ")";
operator =
new EvictingWindowOperator<>(
windowAssigner,
windowAssigner.getWindowSerializer(
getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()),
stateDesc,
function,
trigger,
evictor,
allowedLateness,
lateDataOutputTag);
} else {
ListStateDescriptor stateDesc =
new ListStateDescriptor<>(
"window-contents",
input.getType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()));
opName =
"TriggerWindow("
+ windowAssigner
+ ", "
+ stateDesc
+ ", "
+ trigger
+ ", "
+ udfName
+ ")";
operator =
new WindowOperator<>(
windowAssigner,
windowAssigner.getWindowSerializer(
getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()),
stateDesc,
function,
trigger,
allowedLateness,
lateDataOutputTag);
}
return input.transform(opName, resultType, operator).forceNonParallel();
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window for each key individually. The output of the window function is
* interpreted as a regular non-windowed stream.
*
* Arriving data is incrementally aggregated using the given reducer.
*
* @param reduceFunction The reduce function that is used for incremental aggregation.
* @param function The window function.
* @return The data stream that is the result of applying the window function to the window.
* @deprecated Use {@link #reduce(ReduceFunction, AllWindowFunction)} instead.
*/
@Deprecated
public SingleOutputStreamOperator apply(
ReduceFunction reduceFunction, AllWindowFunction function) {
TypeInformation inType = input.getType();
TypeInformation resultType = getAllWindowFunctionReturnType(function, inType);
return apply(reduceFunction, function, resultType);
}
/**
* Applies the given window function to each window. The window function is called for each
* evaluation of the window for each key individually. The output of the window function is
* interpreted as a regular non-windowed stream.
*
* Arriving data is incrementally aggregated using the given reducer.
*
* @param reduceFunction The reduce function that is used for incremental aggregation.
* @param function The window function.
* @param resultType Type information for the result type of the window function
* @return The data stream that is the result of applying the window function to the window.
* @deprecated Use {@link #reduce(ReduceFunction, AllWindowFunction, TypeInformation)} instead.
*/
@Deprecated
public SingleOutputStreamOperator apply(
ReduceFunction reduceFunction,
AllWindowFunction function,
TypeInformation resultType) {
if (reduceFunction instanceof RichFunction) {
throw new UnsupportedOperationException(
"ReduceFunction of apply can not be a RichFunction.");
}
// clean the closures
function = input.getExecutionEnvironment().clean(function);
reduceFunction = input.getExecutionEnvironment().clean(reduceFunction);
String callLocation = Utils.getCallLocationName();
String udfName = "AllWindowedStream." + callLocation;
String opName;
KeySelector keySel = input.getKeySelector();
OneInputStreamOperator operator;
if (evictor != null) {
@SuppressWarnings({"unchecked", "rawtypes"})
TypeSerializer> streamRecordSerializer =
(TypeSerializer>)
new StreamElementSerializer(
input.getType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()));
ListStateDescriptor> stateDesc =
new ListStateDescriptor<>("window-contents", streamRecordSerializer);
opName =
"TriggerWindow("
+ windowAssigner
+ ", "
+ stateDesc
+ ", "
+ trigger
+ ", "
+ evictor
+ ", "
+ udfName
+ ")";
operator =
new EvictingWindowOperator<>(
windowAssigner,
windowAssigner.getWindowSerializer(
getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()),
stateDesc,
new InternalIterableAllWindowFunction<>(
new ReduceApplyAllWindowFunction<>(reduceFunction, function)),
trigger,
evictor,
allowedLateness,
lateDataOutputTag);
} else {
ReducingStateDescriptor stateDesc =
new ReducingStateDescriptor<>(
"window-contents",
reduceFunction,
input.getType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()));
opName =
"TriggerWindow("
+ windowAssigner
+ ", "
+ stateDesc
+ ", "
+ trigger
+ ", "
+ udfName
+ ")";
operator =
new WindowOperator<>(
windowAssigner,
windowAssigner.getWindowSerializer(
getExecutionEnvironment().getConfig()),
keySel,
input.getKeyType()
.createSerializer(
getExecutionEnvironment()
.getConfig()
.getSerializerConfig()),
stateDesc,
new InternalSingleValueAllWindowFunction<>(function),
trigger,
allowedLateness,
lateDataOutputTag);
}
return input.transform(opName, resultType, operator).forceNonParallel();
}
// ------------------------------------------------------------------------
// Aggregations on the all windows
// ------------------------------------------------------------------------
/**
* Applies an aggregation that sums every window of the data stream at the given position.
*
* @param positionToSum The position in the tuple/array to sum
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator sum(int positionToSum) {
return aggregate(
new SumAggregator<>(positionToSum, input.getType(), input.getExecutionConfig()));
}
/**
* Applies an aggregation that sums every window of the pojo data stream at the given field for
* every window.
*
* A field expression is either the name of a public field or a getter method with
* parentheses of the stream's underlying type. A dot can be used to drill down into objects, as
* in {@code "field1.getInnerField2()" }.
*
* @param field The field to sum
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator sum(String field) {
return aggregate(new SumAggregator<>(field, input.getType(), input.getExecutionConfig()));
}
/**
* Applies an aggregation that gives the minimum value of every window of the data stream at the
* given position.
*
* @param positionToMin The position to minimize
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator min(int positionToMin) {
return aggregate(
new ComparableAggregator<>(
positionToMin,
input.getType(),
AggregationFunction.AggregationType.MIN,
input.getExecutionConfig()));
}
/**
* Applies an aggregation that gives the minimum value of the pojo data stream at the given
* field expression for every window.
*
* A field expression is either the name of a public field or a getter method with
* parentheses of the {@link DataStream}S underlying type. A dot can be used to drill down into
* objects, as in {@code "field1.getInnerField2()" }.
*
* @param field The field expression based on which the aggregation will be applied.
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator min(String field) {
return aggregate(
new ComparableAggregator<>(
field,
input.getType(),
AggregationFunction.AggregationType.MIN,
false,
input.getExecutionConfig()));
}
/**
* Applies an aggregation that gives the minimum element of every window of the data stream by
* the given position. If more elements have the same minimum value the operator returns the
* first element by default.
*
* @param positionToMinBy The position to minimize by
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator minBy(int positionToMinBy) {
return this.minBy(positionToMinBy, true);
}
/**
* Applies an aggregation that gives the minimum element of every window of the data stream by
* the given position. If more elements have the same minimum value the operator returns the
* first element by default.
*
* @param positionToMinBy The position to minimize by
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator minBy(String positionToMinBy) {
return this.minBy(positionToMinBy, true);
}
/**
* Applies an aggregation that gives the minimum element of every window of the data stream by
* the given position. If more elements have the same minimum value the operator returns either
* the first or last one depending on the parameter setting.
*
* @param positionToMinBy The position to minimize
* @param first If true, then the operator return the first element with the minimum value,
* otherwise returns the last
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator minBy(int positionToMinBy, boolean first) {
return aggregate(
new ComparableAggregator<>(
positionToMinBy,
input.getType(),
AggregationFunction.AggregationType.MINBY,
first,
input.getExecutionConfig()));
}
/**
* Applies an aggregation that gives the minimum element of the pojo data stream by the given
* field expression for every window. A field expression is either the name of a public field or
* a getter method with parentheses of the {@link DataStream DataStreams} underlying type. A dot
* can be used to drill down into objects, as in {@code "field1.getInnerField2()" }.
*
* @param field The field expression based on which the aggregation will be applied.
* @param first If True then in case of field equality the first object will be returned
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator minBy(String field, boolean first) {
return aggregate(
new ComparableAggregator<>(
field,
input.getType(),
AggregationFunction.AggregationType.MINBY,
first,
input.getExecutionConfig()));
}
/**
* Applies an aggregation that gives the maximum value of every window of the data stream at the
* given position.
*
* @param positionToMax The position to maximize
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator max(int positionToMax) {
return aggregate(
new ComparableAggregator<>(
positionToMax,
input.getType(),
AggregationFunction.AggregationType.MAX,
input.getExecutionConfig()));
}
/**
* Applies an aggregation that gives the maximum value of the pojo data stream at the given
* field expression for every window. A field expression is either the name of a public field or
* a getter method with parentheses of the {@link DataStream DataStreams} underlying type. A dot
* can be used to drill down into objects, as in {@code "field1.getInnerField2()" }.
*
* @param field The field expression based on which the aggregation will be applied.
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator max(String field) {
return aggregate(
new ComparableAggregator<>(
field,
input.getType(),
AggregationFunction.AggregationType.MAX,
false,
input.getExecutionConfig()));
}
/**
* Applies an aggregation that gives the maximum element of every window of the data stream by
* the given position. If more elements have the same maximum value the operator returns the
* first by default.
*
* @param positionToMaxBy The position to maximize by
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator maxBy(int positionToMaxBy) {
return this.maxBy(positionToMaxBy, true);
}
/**
* Applies an aggregation that gives the maximum element of every window of the data stream by
* the given position. If more elements have the same maximum value the operator returns the
* first by default.
*
* @param positionToMaxBy The position to maximize by
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator maxBy(String positionToMaxBy) {
return this.maxBy(positionToMaxBy, true);
}
/**
* Applies an aggregation that gives the maximum element of every window of the data stream by
* the given position. If more elements have the same maximum value the operator returns either
* the first or last one depending on the parameter setting.
*
* @param positionToMaxBy The position to maximize by
* @param first If true, then the operator return the first element with the maximum value,
* otherwise returns the last
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator maxBy(int positionToMaxBy, boolean first) {
return aggregate(
new ComparableAggregator<>(
positionToMaxBy,
input.getType(),
AggregationFunction.AggregationType.MAXBY,
first,
input.getExecutionConfig()));
}
/**
* Applies an aggregation that gives the maximum element of the pojo data stream by the given
* field expression for every window. A field expression is either the name of a public field or
* a getter method with parentheses of the {@link DataStream}S underlying type. A dot can be
* used to drill down into objects, as in {@code "field1.getInnerField2()" }.
*
* @param field The field expression based on which the aggregation will be applied.
* @param first If True then in case of field equality the first object will be returned
* @return The transformed DataStream.
*/
public SingleOutputStreamOperator maxBy(String field, boolean first) {
return aggregate(
new ComparableAggregator<>(
field,
input.getType(),
AggregationFunction.AggregationType.MAXBY,
first,
input.getExecutionConfig()));
}
private SingleOutputStreamOperator aggregate(AggregationFunction aggregator) {
return reduce(aggregator);
}
// ------------------------------------------------------------------------
// Utilities
// ------------------------------------------------------------------------
public StreamExecutionEnvironment getExecutionEnvironment() {
return input.getExecutionEnvironment();
}
public TypeInformation getInputType() {
return input.getType();
}
}